摘要
以6种不同方式对林相图中同一样带取样,采用CA、DCA和CCA3种排序方法,研究了取样方式对排序轴的解释效果、物种与环境因子、环境因子之间、环境因子与坐标轴之间关系的影响.结果表明,样方大小和形状的变化在不同程度上改变了排序结果.大样方和长方形样方都增强了排序轴的解释效果,并对双序图中稀有种、独特种的位置有较大的影响;环境因子中土壤因子对样方的大小和形状都很敏感,坡度、经纬度只对样方大小敏感,坡位、海拔、温度和降水则对样方形状敏感;随着样方面积的增加,海拔、温度和降水的作用降低,而坡向的作用增加.
The relationships between plant and environment are an important topic in community ecology. To study these relationships, quadrate is often used to sample vegetation and environmental data, and ordination techniques are used to analyze the data. However, the size and shape of quadrate considerably affect the ordination results. To understand this effect remains a research item for various vegetation and environmental data settings. This paper studied the effects of different sampling quadrate （ size and shape） on the results obtained from three common ordination techniques （CA, DCA and CCA）. We did this by sampling the same transect in forest form map six times, using six different quadrates （0.5 km×0.5 km, 0.5 km×2 kin, 2 km×0.5 km, 1 km×l km, 1 km×4 km, 2 km × 2 km）. The results showed that large rectangle quadrate could capture greater percent variance of species data and more information about rare and unique species than small square quadrate. All sizes and shapes of quadrate used in this study had little effect on the dominant species. The captured soil information was sensitive both to the size and to the shape of quadrate, and the information of slope, longitude and latitude was sensitive to the change of quadrate size. Slope position, altitude, temperature and precipitation were sensitive to the change of quadrate shape. Large quadrate reduced the importance of altitude, temperature and precipitation while increased the importance of exposure, but these environmental factors appeared to be important in small quadrate sampling.
出处
《应用生态学报》
CAS
CSCD
北大核心
2006年第9期1563-1569,共7页
Chinese Journal of Applied Ecology
基金
国家自然科学基金重点项目(40331008)
国家自然科学基金项目(40301048)
中国科学院知识创新工程资助项目(KSCX2-SW-133).
关键词
取样方式
林相图
排序
对应分析
去势对应分析
典范对应分析
Sampling mode, Forest form map, Ordination, Correspondence analysis, Detrended correspondenceanalysis, Canonical correspondence analysis.
